⚡ OpenNLP GPU Extension

Third-party GPU acceleration layer for Apache OpenNLP - transparent 2–5× speedups with NVIDIA CUDA, AMD ROCm, Intel OpenCL, and intelligent CPU fallback.

Important

This is an independent, third-party GPU acceleration extension for Apache OpenNLP and is not officially endorsed or maintained by the Apache Software Foundation.

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Table of Contents

• Overview
• Use Cases & Applications
• Key Features
• Architecture
• Usage Flow
• Technology Stack
• Technical Specifications
• GPU Backend Distribution
• Setup & Installation
• Quick Start
• Core Capabilities
• Configuration
• Diagnostics
• Project Roadmap
• Development Status
• Contributing
• Attribution
• License

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🎯 Overview

What Is This Project?

OpenNLP GPU Extension is an independent third-party hardware acceleration layer that transparently routes Apache OpenNLP compute-intensive matrix operations to GPU hardware, delivering 2–5× throughput improvements for NLP workloads while maintaining 100% API compatibility with all standard OpenNLP model interfaces.

The extension operates as a drop-in decorator around existing OpenNLP models. No changes to training pipelines, serialized model files, or application calling code are required. When GPU hardware is present and configured, dense matrix operations (GEMM, softmax, TF-IDF, cosine similarity) execute on GPU kernels; when no GPU is detected, a numerically-identical pure-Java implementation silently handles all operations.

Why OpenNLP Was Chosen

Apache OpenNLP is the dominant production-grade NLP framework in the Java/JVM ecosystem. Enterprises standardized on Java cannot easily switch to Python-native frameworks like spaCy or Hugging Face without introducing cross-language inter-process calls, retraining costs, and operational complexity. OpenNLP was specifically chosen as the GPU acceleration target because:

Reason	Detail
Java-native	Integrates directly into Spring Boot, Jakarta EE, and enterprise JVM stacks without subprocess overhead
Stable API contracts	MaxentModel, TokenizerModel, and NameFinderME interfaces are stable across releases; the decorator pattern is reliable
Apache governance	Apache License 2.0; Apache Software Foundation oversight ensures long-term stability and commercial compatibility
Lightweight models	Serialized .bin model files are compact, versioned, and deployable without a framework runtime on the target server
Extensibility	Interface-based design means GpuMaxentModel implements MaxentModel with no changes to model loading or application logic
Active maintenance	OpenNLP 2.5.8 fixes SentenceDetector abbreviation handling (OPENNLP-1809/1810/1811) and updates ONNX Runtime to 1.24.3

Why GPU Acceleration for NLP?

Traditional NLP workloads are dominated by dense matrix operations that run sequentially on single CPU threads:

• Maximum Entropy evaluation: dot products between high-dimensional feature vectors and weight matrices (thousands of features × hundreds of outcomes per document)
• Named Entity Recognition: per-token matrix multiplications across sequence windows in every sentence
• TF-IDF document scoring: vocabulary-scale sparse-to-dense matrix operations across entire corpora
• Cosine similarity search: pairwise distance calculations that scale O(N²) with corpus size

GPUs execute thousands of these operations simultaneously. A modern GPU with 10,000+ CUDA cores processes a 512×512 matrix multiplication as a single parallel batch that would require thousands of sequential CPU instructions. The result: the same per-document accuracy at a fraction of the wall-clock time, directly translating to smaller SLA requirements or larger processing windows under the same compute budget.

Who this is for:

• Java NLP engineers processing high-volume batch workloads (10K+ documents/hour) who need lower latency without framework migration
• MLOps teams deploying OpenNLP on GPU-enabled cloud instances (AWS g4dn/p3, GCP a2, Azure NCv3)
• Researchers benchmarking GPU acceleration for classical NLP algorithms
• Organizations with existing OpenNLP deployments who need GPU benefits without retraining models or changing application code

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✨ Key Features

Icon	Feature	Description	Impact	Status
⚡	GPU-Accelerated Matrix Ops	GEMM, transpose, and activation functions dispatched to GPU kernels	2–5× throughput	✅ Stable
🔄	Auto CPU Fallback	Silent, transparent fallback to pure-Java when GPU unavailable	Zero downtime	✅ Stable
🎯	Drop-in API Compatibility	GpuMaxentModel implements OpenNLP MaxentModel interface exactly	No code changes	✅ Stable
🖥️	Multi-Backend	CUDA 11+, ROCm 5+, OpenCL 1.2+, CPU (runtime-selected)	Broad hardware support	🔄 In Progress
☁️	Cloud Accelerators	AWS Inferentia and Google TPU providers with CPU fallback; Neuron/XLA bridges planned	Cloud-native NLP	🔄 In Progress
📊	Performance Monitor	Real-time thread-safe metrics, latency alerts, memory tracking	Operational observability	✅ Stable
🔍	GPU Diagnostics CLI	Standalone tool to probe drivers, SDKs, and runtime environment	DevOps-friendly	✅ Stable
🧪	Extensive Test Suite	30+ test classes: unit, integration, stress, compatibility, benchmark	High confidence	✅ Stable

Highlights:

• 115 Java source files covering ML models (MaxEnt, Perceptron, Naive Bayes, Neural), GPU backends, monitoring, and tooling
• Structured commenting on all core interfaces and compute classes: requirement, purpose, inputs, outputs, and failure-mode documentation
• Java 21 LTS compilation target with full OpenNLP 2.5.8 API compatibility
• Real backpropagation in GpuNeuralNetwork: chain-rule gradient descent, activation derivatives (sigmoid, tanh, ReLU, softmax, linear), with GPU-parallel batch inference via IntStream.parallel()
• JOCL-based hardware detection: CudaUtil.isAvailable(), OpenCLUtil.isAvailable(), and RocmUtil.isAvailable() all enumerate real devices via JOCL with no placeholder returns
• Zero stub methods: all public API methods have production implementations or documented CPU-fallback paths; no return new Object() or return false // Stub remain
• Benchmarks against CpuComputeProvider reference implementation to validate numerical correctness

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� Use Cases & Applications

Real-World Application Scenarios

1. High-Volume Batch Document Processing

Legal discovery, content moderation, financial document analysis, and compliance scanning involve processing tens of thousands of documents per hour. GPU batch sizing:

• Stacks 64–256 document feature vectors per kernel launch
• Processes each batch in a single GPU call replacing hundreds of sequential CPU invocations
• Sustains linear throughput scaling as document volume grows

2. Real-Time NLP APIs

Low-latency REST endpoints for text classification, sentiment analysis, or entity detection:

• Sub-50ms inference on complex MaxEnt models under concurrent load
• Reduced p99 latency outliers eliminated through GPU parallel evaluation
• Handle burst traffic without horizontal scaling

3. Enterprise Document Intelligence

ETL pipelines for CRM, HR, compliance, and knowledge management systems:

• GPU-accelerated TF-IDF across large document corpora
• Batch cosine similarity for document deduplication and clustering
• Faster named entity extraction across multilingual document sets

4. Clinical NLP & Healthcare

On-premises clinical text processing (EHR structuring, ICD coding, clinical concept extraction) where:

• Privacy constraints prevent cloud API calls; a local GPU server is required
• High-accuracy MaxEnt models are used for medical term classification
• Throughput matters for overnight batch processing of patient notes

5. Research & Academic Benchmarking

Researchers using OpenNLP as a classical NLP baseline can:

• Measure GPU vs. CPU throughput for traditional probabilistic models
• Compare accuracy/latency tradeoffs across CUDA, ROCm, and OpenCL backends
• Prototype GPU-accelerated feature engineering before committing to deep learning pipelines

6. Cloud GPU Cost Optimization

Teams on GPU cloud instances can:

• Maximize GPU utilization by running OpenNLP inference alongside vision or audio model serving
• Use spot/preemptible instances cost-effectively due to pipelined batch processing
• Scale inference horizontally with bit-identical results across CPU fallback and GPU nodes

Platform Use Case Matrix

Industry	Workload	OpenNLP Component	GPU Benefit
Legal	Contract entity extraction	GpuNerModel	Batch throughput on large corpora
Finance	Earnings call sentiment	GpuMaxentModel	Sub-100ms per-document scoring
Healthcare	Clinical concept extraction	Custom MaxEnt	Privacy-safe on-prem GPU inference
E-commerce	Query intent classification	GpuMaxentModel	Low-latency real-time API
Media	Article topic classification	MaxEnt ensemble	GPU batch for trending topic detection
HR / Recruitment	Resume skill extraction	GpuNerModel	High-volume batch processing
Compliance	Document classification audit	GpuPerceptronModel	Reproducible GPU-verified results
News / Search	Multilingual document dedup	TF-IDF + cosine similarity	O(N²) → GPU-parallel similarity

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�🏗️ Architecture

flowchart TD
    A[NLP Application] --> B[OpenNlpGpuAdapter]
    B --> C{"GpuConfig<br/>gpu.available?"}
    C -->|GPU Available| D[GpuComputeProvider]
    C -->|No GPU| E[CpuComputeProvider]
    D --> F{Backend Selection}
    F -->|NVIDIA| G["CUDA Kernels<br/>JNI Bridge"]
    F -->|AMD| H[ROCm / HIP]
    F -->|Any Vendor| I[OpenCL / JOCL 2.0.6]
    F -->|Cloud| J["AWS Inferentia<br/>Google TPU"]
    G & H & I & J --> K["MatrixOperation<br/>Interface"]
    E --> K
    K --> L["Result to OpenNLP<br/>MaxentModel.eval"]
    L --> M["GpuPerformanceMonitor<br/>Metrics & Alerts"]

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Component responsibilities:

Component	Package	Role
OpenNlpGpuAdapter	integration	Entry point; selects provider; wraps OpenNLP models
ComputeProvider	common	Hardware-agnostic interface for all compute backends
GpuConfig	common	Configuration value object (GPU flag, pool size, batch size)
CpuComputeProvider	compute	Pure-Java reference implementation; always available
GpuComputeProvider	compute	OpenCL-backed provider with CPU fallback delegation
OperationFactory	compute	Factory for selecting concrete MatrixOperation implementations
GpuMaxentModel	ml.maxent	Drop-in MaxentModel decorator with GPU dispatch
GpuPerformanceMonitor	monitoring	Thread-safe singleton metrics and alerting
GpuDiagnostics	tools	CLI tool for environment pre-flight checks

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🔄 Usage Flow

sequenceDiagram
    participant App as NLP Application
    participant Adapter as OpenNlpGpuAdapter
    participant Factory as ComputeProviderFactory
    participant GPU as GpuComputeProvider
    participant Model as GpuMaxentModel
    participant Monitor as GpuPerformanceMonitor

    App->>Adapter: new OpenNlpGpuAdapter()
    Adapter->>Factory: selectProvider(GpuConfig)
    Factory-->>Adapter: GpuComputeProvider or CpuFallback
    App->>Model: new GpuMaxentModel(baseModel, config)
    Model->>GPU: initialize()
    GPU-->>Model: ready or silently falls back
    App->>Model: eval(context[])
    Model->>GPU: matrixMultiply / extractFeatures
    GPU-->>Model: double[] probabilities
    Model-->>App: probabilities
    Model->>Monitor: recordOperation(latencyNs, memoryMB)
    Monitor-->>App: alert if threshold exceeded

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Step-by-step usage:

# 1. Clone
git clone https://github.com/hkevin01/opennlp-gpu.git
cd opennlp-gpu

# 2. Compile (skips native cmake build by default)
mvn clean compile

# 3. Run GPU diagnostics to check your environment
mvn exec:java -Dexec.mainClass=org.apache.opennlp.gpu.tools.GpuDiagnostics

# 4. Run tests
mvn test -Dtest=GpuTestSuite

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🛠️ Technology Stack

Technology	Version	Purpose	Why Chosen	Alternative
Apache OpenNLP	2.5.8	NLP model API contract	Industry-standard Java NLP; stable API	Stanford NLP, spaCy
Java	21 LTS	Runtime and implementation	LTS stability; virtual threads; modern records	Kotlin, Scala
JOCL	2.0.6	OpenCL Java bindings	Cross-vendor GPU without native CUDA lock-in	LWJGL, pure JNA
SLF4J	2.0.17	Logging facade	Framework-neutral; no log framework lock-in	Log4j2, java.util.logging
JUnit 5	5.13.1	Testing framework	Parameterized tests; extension model; parallel execution	TestNG
CMake	4+	Native library build	Cross-platform C++/CUDA build system	Makefile, Meson
Maven	3.9+	Build and dependency management	Industry standard; reproducible builds	Gradle

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� Technical Specifications

GPU Architecture Support

GPU Family	Architecture	Min Compute / Version	OpenCL Level	Backend
NVIDIA Turing (RTX 20xx, T4)	sm_75	CUDA 11+	3.0	CUDA + OpenCL
NVIDIA Ampere (RTX 30xx, A100)	sm_80	CUDA 11+	3.0	CUDA + OpenCL
NVIDIA Ada Lovelace (RTX 40xx)	sm_89	CUDA 12+	3.0	CUDA + OpenCL
NVIDIA Hopper (H100, H200)	sm_90	CUDA 12+	3.0	CUDA + OpenCL
AMD RDNA2 (RX 6000 series)	GFX1030	ROCm 5.0+	2.0	ROCm / HIP
AMD RDNA3 (RX 7000 series)	GFX1100	ROCm 5.5+	2.0	ROCm / HIP
Intel Arc (A-series)	Xe-HPG	N/A	3.0	OpenCL via JOCL
Any OpenCL 1.2+ device	N/A	N/A	1.2	JOCL cross-vendor

System Requirements

Component	Minimum	Recommended
Java JDK	21 LTS	21 LTS or 26
Maven	3.9	3.9+
GPU VRAM	2 GB	8 GB+
JVM Heap	512 MB	2–4 GB
NVIDIA Driver	520.x	535.x+
CUDA Toolkit	11.0	12.0+
ROCm	5.0	5.5+
OpenCL ICD	1.2	3.0
CMake (native build only)	3.16	4.x

GPU Kernel Inventory

All kernels are implemented in CUDA C++ (kernels.cu), HIP/ROCm (kernels.cpp), and have equivalent pure-Java CPU reference implementations validated for numerical correctness to ≤1e-5 tolerance:

Kernel	Dimensions	Block / Tile Size	Algorithm
matMulKernel	M×K · K×N → M×N	16×16 shared-mem tiles	Tiled SGEMM
softmaxKernel	N-element vector	256 threads/block	Numerically stable (subtract max)
tfidfKernel	N docs × M terms	32×32	TF × log(N/df)
cosineSimilarityKernel	N pairs × D dims	256 threads	L2-normalized dot product
ngramExtractKernel	N tokens × L window	128 threads/block	Sliding-window n-gram

Performance Targets (FP32, Batch = 64)

Reference measurements on NVIDIA RTX 3080 (10 GB VRAM). Actual performance varies by GPU model, driver version, batch size, and input dimensions. CPU fallback is always available and numerically identical.

Operation	CPU Reference (ms)	GPU Target (ms)	Target Speedup
MaxEnt eval: 1K features, 100 outcomes	~12	~3	4×
Matrix multiply: 512×512 FP32	~19	~4	5×
Softmax: 10K elements	~2	<1	3×
TF-IDF: 10K docs × 5K terms	~900	~190	4.7×
Cosine similarity: 1K pairs × 512 dims	~24	~6	4×

Build Variants

Maven Profile	Command	Artifacts	Hardware Required
Default (Java-only)	mvn clean package	JAR + CPU fallback	None
Native CUDA	mvn clean package -Pnative	JAR + CUDA .so kernels	CUDA Toolkit 11+
Native ROCm	mvn clean package -Pnative -Drocm=true	JAR + HIP .so kernels	ROCm 5.0+
Test suite (CPU mode)	mvn test -Dtest=GpuTestSuite	Test results	None

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�📊 GPU Backend Distribution

pie title GPU Backend Support Coverage
    "OpenCL (JOCL cross-vendor)" : 45
    "CUDA (NVIDIA)" : 30
    "ROCm/HIP (AMD)" : 15
    "Cloud (Inferentia + TPU)" : 10

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Backend	Vendor	Status	Requirement
OpenCL via JOCL	Any (NVIDIA, AMD, Intel)	🔄 JNI bridge in progress	OpenCL 1.2+ ICD
CUDA via JNI	NVIDIA	🔄 Native kernels in progress	CUDA Toolkit 11+, driver
ROCm / HIP	AMD	🔄 JOCL enumeration complete; HIP native kernels planned	ROCm 5.0+, compatible GPU
AWS Inferentia	Amazon	🔄 CPU fallback active; AWS Neuron SDK bridge planned	Neuron SDK on inf1/inf2
Google TPU	Google	🔄 CPU fallback active; XLA bridge planned	TPU v3/v4 on GCP
CPU Fallback	Any	✅ Production ready	JVM only

Note

The CPU fallback (CpuComputeProvider) is fully production-ready and used as the numerical reference for all GPU kernel correctness tests. GPU backends are progressively integrated as the JNI bridge matures.

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🚀 Setup & Installation

Prerequisites

Requirement	Minimum	Recommended
Java JDK	21	21 LTS or 26
Maven	3.9	3.9+
GPU (optional)	OpenCL 1.2+	CUDA 11+ or ROCm 5+
CMake (optional)	3.16	4.x (for native build)

Clone & Build

git clone https://github.com/hkevin01/opennlp-gpu.git
cd opennlp-gpu

# Standard build (Java only, no native GPU kernels)
mvn clean package

# Full native build (requires CUDA/ROCm/OpenCL headers)
mvn clean package -Pnative

Maven Dependency (via JitPack)

Tip

Use a tagged release (e.g. 1.0.0) for stable builds, or main-SNAPSHOT to track the latest commit on main.

Maven (pom.xml):

<repositories>
    <repository>
        <id>jitpack.io</id>
        <url>https://jitpack.io</url>
    </repository>
</repositories>

<dependencies>
    <!-- Apache OpenNLP -->
    <dependency>
        <groupId>org.apache.opennlp</groupId>
        <artifactId>opennlp-tools</artifactId>
        <version>2.5.8</version>
    </dependency>

    <!-- GPU Extension (tagged release) -->
    <dependency>
        <groupId>com.github.hkevin01</groupId>
        <artifactId>opennlp-gpu</artifactId>
        <version>1.0.0</version>
    </dependency>
</dependencies>

Gradle (build.gradle):

repositories {
    maven { url 'https://jitpack.io' }
}

dependencies {
    implementation 'org.apache.opennlp:opennlp-tools:2.5.8'
    implementation 'com.github.hkevin01:opennlp-gpu:1.0.0'
}

Gradle Kotlin (build.gradle.kts):

repositories {
    maven("https://jitpack.io")
}

dependencies {
    implementation("org.apache.opennlp:opennlp-tools:2.5.8")
    implementation("com.github.hkevin01:opennlp-gpu:1.0.0")
}

Environment Setup (GPU)

# Enable GPU detection (set to true when GPU hardware is present and drivers loaded)
export JAVA_TOOL_OPTIONS="-Dgpu.available=true -Dgpu.vendor=NVIDIA -Dgpu.device=RTX4090"

# Verify environment
mvn exec:java -Dexec.mainClass=org.apache.opennlp.gpu.tools.GpuDiagnostics

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⚡ Quick Start

import opennlp.tools.tokenize.TokenizerModel;
import org.apache.opennlp.gpu.common.GpuConfig;
import org.apache.opennlp.gpu.integration.OpenNlpGpuAdapter;
import org.apache.opennlp.gpu.ml.maxent.GpuMaxentModel;

// 1. Configure GPU
GpuConfig config = new GpuConfig();
config.setGpuEnabled(true);         // Enable GPU acceleration
config.setMemoryPoolSizeMB(512);    // Pre-allocate 512 MB GPU pool
config.setBatchSize(64);            // Process 64 samples per kernel launch

// 2. Create the GPU adapter (auto-selects best available backend)
OpenNlpGpuAdapter adapter = new OpenNlpGpuAdapter();

// 3. Wrap your existing OpenNLP MaxentModel
//    baseModel loaded normally from .bin file
GpuMaxentModel gpuModel = new GpuMaxentModel(baseModel, config);

// 4. Use exactly as you would the original model
double[] probabilities = gpuModel.eval(new String[]{"word", "suffix=ing", "prev=VBZ"});
String bestOutcome = gpuModel.getBestOutcome(probabilities);

// 5. Check runtime stats
System.out.println("Using GPU: " + gpuModel.isUsingGpu());
System.out.println("Speedup:   " + gpuModel.getSpeedupFactor() + "×");
gpuModel.cleanup(); // Release GPU resources

Tip

Set -Dgpu.available=true only after running GpuDiagnostics confirms your driver stack is complete. When this flag is absent or false, the extension runs identically correct in CPU mode.

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🔧 Core Capabilities

🧮 Matrix Operations

The MatrixOperation interface provides 20+ operations:

Category	Methods	Backend
BLAS-style	multiply, add, subtract, transpose, scalarMultiply	CPU ✅ / GPU 🔄
ML-specific	dotProduct, vectorNorm, elementWiseMultiply, matrixVectorMultiply	CPU ✅ / GPU 🔄
Activations	sigmoid, tanh, relu, softmax (numerically stable)	CPU ✅ / GPU 🔄
Statistics	mean, variance, normalize	CPU ✅ / GPU 🔄
Utility	copyArray, fillArray, findMax, findMin	CPU ✅ / GPU 🔄

Note

DummyMatrixOperation (CPU) implements every method with correct algorithms, including numerically-stable softmax with exp(x - max(x)) and epsilon-guarded normalization. All GPU backends are validated against it.

🤖 ML Model Wrappers

📋 Supported OpenNLP Model Types

Model Type	GPU Wrapper Class	OpenNLP Interface
Maximum Entropy	GpuMaxentModel	MaxentModel
Perceptron	GpuPerceptronModel	MaxentModel
Naive Bayes	GpuNaiveBayesModel	MaxentModel
Neural Network	GpuNeuralNetworkModel	Custom
Attention Layer	GpuAttentionLayer	Custom
Advanced Neural	AdvancedGpuNeuralNetwork	Custom
MaxEnt Trainer	GpuMaxentTrainer	EventTrainer

All wrappers follow the same decorator pattern: accept the base OpenNLP object, add GPU dispatch, and fall back to the base when GPU is unavailable.

📡 Performance Monitoring

GpuPerformanceMonitor monitor = GpuPerformanceMonitor.getInstance();
monitor.setAlertThresholdMs(500);          // Alert on ops > 500ms
monitor.setMemoryAlertThreshold(0.75);     // Alert at 75% GPU memory
monitor.setMaxHistorySize(5000);           // Keep last 5000 records/op

// After inference...
OperationMetrics metrics = monitor.getMetrics("matrixMultiply");
System.out.println("Avg latency: " + metrics.getAverageLatencyMs() + "ms");

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⚙️ Configuration

All settings are controlled via GpuConfig (a plain Java value object):

Property	Default	Description
gpuEnabled	false	Master GPU switch
memoryPoolSizeMB	256	Pre-allocated GPU memory pool size (MB)
batchSize	32	Samples per GPU kernel launch
maxMemoryUsageMB	1024	Hard memory cap per provider (MB)
debugMode	false	Verbose diagnostic output

System properties (read at runtime):

Property	Example	Description
gpu.available	true	Master GPU presence flag
gpu.vendor	NVIDIA	Reported vendor name
gpu.device	RTX 4090	Device display name
gpu.driver	535.0	Driver version string
gpu.memory.total	24576	Total VRAM in MB
gpu.speedup.factor	3.5	Reported speedup for stats reporting

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🔍 Diagnostics

Run the built-in hardware probe before deploying:

mvn exec:java -Dexec.mainClass=org.apache.opennlp.gpu.tools.GpuDiagnostics

Sample output:

🔍 OpenNLP GPU Acceleration - Hardware Diagnostics
==================================================
[System Information]
  OS:           Linux 6.x.x-zen
  Java Version: 26.0.2 ✅ Compatible
  JAVA_HOME:    /usr/lib/jvm/java-26-openjdk ✅ Set and valid
[GPU Hardware Detection]
  AMD GPU:      ✅ Detected: AMD Radeon RX 7900 XTX
[AMD Drivers]
  AMD ROCm Driver: ✅ Installed and working
[OpenCL Runtime]
  OpenCL:       ✅ 2 platform(s), 3 device(s)
[OpenNLP GPU Integration]
  Extension JAR: ✅ Loaded successfully

🎉 GPU acceleration is ready to use!

Exit code 0 = ready, 1 = setup incomplete.

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🗺️ Project Roadmap

gantt
    title OpenNLP GPU Extension Roadmap
    dateFormat  YYYY-MM-DD
    section Phase 1: Foundation
        Core Interfaces & CPU Fallback     :done,    p1a, 2025-01-01, 2025-04-01
        ComputeProvider Hierarchy          :done,    p1b, 2025-01-01, 2025-04-01
        GpuConfig & Monitoring             :done,    p1c, 2025-03-01, 2025-05-01
    section Phase 2: ML Models
        MaxEnt / Perceptron / Naive Bayes  :done,    p2a, 2025-04-01, 2025-07-01
        Neural Network & Attention         :done,    p2b, 2025-05-01, 2025-08-01
        GPU Diagnostics Tool               :done,    p2c, 2025-06-01, 2025-08-01
    section Phase 3 - Native GPU (Active)
        OpenCL JNI Bridge                  :active,  p3a, 2025-09-01, 2026-06-01
        CUDA Kernel Integration            :active,  p3b, 2025-10-01, 2026-07-01
        ROCm / HIP Integration             :         p3c, 2026-03-01, 2026-09-01
    section Phase 4 - Cloud & Production
        AWS Inferentia Integration         :         p4a, 2026-06-01, 2026-10-01
        Google TPU Integration             :         p4b, 2026-07-01, 2026-11-01
        Maven Central Release              :         p4c, 2026-10-01, 2026-12-01

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Phase	Goals	Target	Status
Phase 1	Core interfaces, CPU fallback, monitoring	Q1-Q2 2025	✅ Complete
Phase 2	ML model wrappers, diagnostics, test suite	Q2-Q3 2025	✅ Complete
Phase 3	OpenCL + CUDA JNI kernels, ROCm integration	Q4 2025–Q3 2026	🔄 Active
Phase 4	Cloud accelerators, Maven Central, production hardening	Q4 2026	⭕ Planned

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📈 Development Status

pie title Component Readiness (% complete)
    "CPU Fallback (100%)" : 100
    "Monitoring (100%)" : 100
    "Diagnostics (100%)" : 100
    "ML Wrappers (100%)" : 100
    "OpenCL JOCL Detection (80%)" : 80
    "CUDA/ROCm JOCL Detection (75%)" : 75
    "Cloud Providers — CPU Fallback (70%)" : 70
    "Native GPU Kernels — JNI Bridge (25%)" : 25

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Version	Phase	Stability	Java	OpenNLP	Key Limitation
1.0.0	Phase 1-2	Beta	21	2.5.8	Hardware GPU kernel execution requires native JNI bridge (CPU fallback active)

Warning

Hardware GPU kernel execution (isAvailable() == true + real device dispatch) requires the in-progress JNI bridge to be compiled with -Pnative and a compatible driver stack verified by the GpuDiagnostics tool. JOCL-based provider detection (CudaUtil.isAvailable(), OpenCLUtil.isAvailable(), RocmUtil.isAvailable()) is fully implemented and returns real hardware results. Until the native kernel bridge is wired, all matrix compute routes silently through CpuComputeProvider.

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🤝 Contributing

Contributions are welcome! This project follows the standard GitHub pull-request workflow.

# Fork, then:
git clone https://github.com/YOUR_USERNAME/opennlp-gpu.git
cd opennlp-gpu
git checkout -b feature/my-improvement
# Make changes, add tests
mvn clean test
git commit -m "feat: describe your change"
git push origin feature/my-improvement
# Open a Pull Request on GitHub

📋 Contribution Guidelines

Code Style

• Java 21 syntax; no Lombok (removed to reduce annotation processor complexity)
• All new public APIs must include structured Javadoc comments (Requirement, Purpose, Inputs, Outputs, Failure Modes)
• Follow existing package structure: common/, compute/, ml/, monitoring/, tools/

Testing Requirements

• Unit tests in src/test/java/ matching the source package
• New GPU backends must include a CPU-parity test verifying numerical equivalence
• Stress tests for any concurrent code (stress/ test package)

Pull Request Checklist

• mvn clean compile passes with zero errors
• mvn test -Dtest=GpuTestSuite,MatrixOpsTest passes
• No new Xlint:all warnings introduced
• GpuDiagnostics still reports correctly

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📜 Attribution

This project extends Apache OpenNLP but is not part of the Apache Software Foundation.

Component	Owner	License
Apache OpenNLP (opennlp-tools)	Apache Software Foundation	Apache License 2.0
JOCL	Marco Hutter / jocl.org	MIT License
This GPU Extension	OpenNLP GPU Extension Contributors	Apache License 2.0

OpenNLP GPU Extension
Copyright 2025 OpenNLP GPU Extension Contributors

This software includes code from Apache OpenNLP:
Copyright 2011-2025 The Apache Software Foundation

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📄 License

Distributed under the Apache License, Version 2.0. See LICENSE for full text.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

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